Dynamic Load Balancing Based on Question Difficulty

ABSTRACT

Mechanisms are provided for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines. The mechanisms receive an input question for processing by the QA system and determine a predicted question difficulty of the input question. The mechanisms select a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty and route the input question to the selected QA system pipeline for processing. In addition, the mechanisms process the input question with the selected QA system pipeline to generate an answer for the input question.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for performingdynamic load balancing based on question difficulty.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems which may take an input question, analyze it, and return resultsindicative of the most probable answer to the input question. QA systemsprovide automated mechanisms for searching through large sets of sourcesof content, e.g., electronic documents, and analyze them with regard toan input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

One such QA system is the Watson™ system available from InternationalBusiness Machines (IBM) Corporation of Armonk, N.Y. The Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The Watson™system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypothesis based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypothesis, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

Various United States Patent Application Publications describe varioustypes of question and answer systems. U.S. Patent ApplicationPublication No. 2011/0125734 discloses a mechanism for generatingquestion and answer pairs based on a corpus of data. The system startswith a set of questions and then analyzes the set of content to extractanswer to those questions. U.S. Patent Application Publication No.2011/0066587 discloses a mechanism for converting a report of analyzedinformation into a collection of questions and determining whetheranswers for the collection of questions are answered or refuted from theinformation set. The results data are incorporated into an updatedinformation model

SUMMARY

In one illustrative embodiment, a method, in a data processing system,for performing load balancing of question processing in a Question andAnswer (QA) system, implemented by the data processing system, having aplurality of QA system pipelines. The method comprises receiving, by thedata processing system, an input question for processing by the QAsystem and determining a predicted question difficulty of the inputquestion. The method further comprises selecting a QA system pipelinefrom the plurality of QA system pipelines based on the predictedquestion difficulty and routing the input question to the selected QAsystem pipeline for processing. In addition, the method comprisesprocessing the input question by the selected QA system pipeline togenerate an answer for the input question.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion and answer (QA) system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment;

FIG. 4 is an example diagram of the primary operational components, andtheir operation, during a training phase of operation of a QA system inaccordance with one illustrative embodiment;

FIG. 5 is an example diagram illustrating the primary operationalelements of a QA system during runtime operation using questiondifficulty prediction rules generated as part of a training operation inaccordance with one illustrative embodiment;

FIG. 6 is a flowchart outlining an example operation for performingquestion difficulty prediction training of a QA system in accordancewith one illustrative embodiment; and

FIG. 7 is a flowchart outlining an example operation for performingruntime load balancing routing based on predicted question difficulty inaccordance with one illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for performing dynamicload balancing based on question difficulty. That is, through trainingof a Question and Answer (QA) system, comprising a plurality of QAsystem pipelines, each pipeline being associated with separateprocessors or computing devices, features of input questions areextracted and the processing load for processing each input question isdetermined. This information may be used to generate load balancingpatterns or rules that may be used to predict the load on the QA systemwhen subsequent input questions having similar features are processed.Based on such predictions, and the current loading of the variousprocessors or computing devices associated with the QA system pipelines,load balancing algorithms may be utilized to cause input questions to berouted to appropriate QA system pipelines based on the predicteddifficulty of processing the input question so as to balance the loadacross the processors or computing devices of the QA system.

Systems are typically load balanced by spreading work across variousnodes, e.g., processors or computing devices, in parallel. The sametechnique may be used by a QA system, such as the Watson™ QA system,such that input questions may be distributed to various nodes that thenanswer the input questions. This technique works well for systems whenthe units of work all require a similar amount of time to process.However, when the units of work vary, the load balancing offered by thisapproach is less than optimal. With a QA system, for example, eachquestion processed could take a vastly different amount of time toprocess since some questions take less or more processing cycles toarrive at an answer having a sufficiently high confidence measure. Theillustrative embodiments provide a solution to such load balancingissues by taking into account the difficulty of processing the inputquestion, as determined based on features extracted from the inputquestion and previous training of the QA system based on questiondifficulty, i.e. difficulty of processing the input questions.

With the mechanisms of the illustrative embodiments, during a trainingphase of a QA system, as training questions are submitted to the QAsystem, they are analyzed and broken down to extract features of theinput question. These features represent metadata of the input questionincluding number of sentences in the question, number of words in eachsentence, overall length of the question, the length of the words in thequestion, domain specific question artifacts (e.g., domain type (e.g.,medical, insurance, or pop culture) and sub-domain type (e.g., cancer,automobile insurance, or singers), and the like. Moreover, the featuresmay further include information regarding time requirements, resourcerequirements (e.g., memory usage and processor usage requirements) toextract these other features. Furthermore, the features may includesemantic elements of the input question typically extracted by a QAsystem when analyzing the input question for purposes of generatinganswers to the input question, e.g., the Lexical Answer Type (LAT), theQuestion Classification (QClass), Question Sections (QSections), and thelike. These semantic elements are used to determine what parts of thequestion are important for processing, the type of answer required, andrestraints on the answer.

After extracting these features from the input question, the inputquestion is submitted to the QA system pipeline for generating an answerto the input question. When the input question is submitted to the QAsystem pipeline, timing data and/or resource usage data is collected todetermine how long and/or how much of the resources a particularquestion used to arrive at an answer having a confidence measure above apredetermined minimum threshold confidence measure. For purposes of thefollowing description, it will be assumed that timing data is primarilyused when determining a difficulty of a question and how to load balancequestions submitted. However, as noted above, it should be appreciatedthat other resource usage data may be used instead of, or in additionto, this timing data to assist in predicting how difficult a questionwill be to answer and then perform load balancing based on thisprediction.

Assuming an embodiment in which timing data is primarily utilized toidentify difficulty of a question, the extracted features are correlatedwith this timing data to generate a pattern or rule indicating theextracted features and the resulting timing data. In some illustrativeembodiments, the extracted features are correlated with timing data fora plurality of input training questions and groups of training questionshaving similar patterns of features are identified. From this groupingof training questions, the corresponding timing data may be used togenerate a representation of a difficulty of processing questions havinga similar pattern of features. The generated pattern or rule may then beapplied during runtime operation (after training of the QA system hascompleted) to predict processing time requirements for subsequentlysubmitted input questions. These processing time requirements areindicative of a level of difficulty of the processing of the inputquestion. Moreover, as described hereafter, in some illustrativeembodiments, a categorization of difficulty level may be assigned toquestions having a particular pattern of features such that the categoryof difficulty may be used to predict the difficulty of the question.Based on this timing data, or indication of a general category ofdifficulty, load balancing may be performed amongst the variousprocessors or computing devices, hereafter referred to collectively as“nodes”, based on this predicted difficulty in generating an answer forthe input question.

Thus, by learning such patterns of features in input questions and theircorresponding timing data, by training the QA system, similar patternsof features may be identified in subsequent input questions and aprediction that such input questions are likely to require a similaramount of time to process the input question and generate an answerhaving a minimum threshold level of confidence. For example, through thelearning process, a feature pattern of the type that questions withthree or more words longer than 8 characters whose LAT is not “X”, “Y”,or “Z” (where X, Y, and Z may be different lexical answer types), andwhere the QClass took longer than N miliseconds to compute take 40%longer to answer than questions without these features. Such a featurepattern or rule may be generated through analysis of a plurality ofcorrelations between extracted feature patterns and timing data for aplurality of input questions submitted to the QA system during thetraining phase. Moreover, the feature patterns or rules may be specificto particular domains of subject matter since the length of theprocessing time may be dependent on the metadata gathered in thetraining phase with respect to the subject matter domain. In this way,the load balancing may be tailored to the subject matter domain.

The mechanisms of the illustrative embodiments allow the QA system toperform more intelligent routing decisions about where to send aparticular input question to meet throughput and overall service levelagreement requirements for answering questions. Because the routingdecision is made on calculated question difficulty and current systemload, questions may be routed to a system that has more or less loadthan another. For example, system A may have three easy questions andsystem B may have one hard question. If a subsequent question isdetermined to be easy, it may be routed to system A if it is determinedthat time requirements for processing the combination of the easyquestions in system A balances with the time for processing the singlehard question in system B.

The above aspects and advantages of the illustrative embodiments of thepresent invention will be described in greater detail hereafter withreference to the accompanying figures. It should be appreciated that thefigures are only intended to be illustrative of exemplary embodiments ofthe present invention. The present invention may encompass aspects,embodiments, and modifications to the depicted exemplary embodiments notexplicitly shown in the figures but would be readily apparent to thoseof ordinary skill in the art in view of the present description of theillustrative embodiments.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in any one or more computer readablemedium(s) having computer usable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium is a system, apparatus, or device of an electronic,magnetic, optical, electromagnetic, or semiconductor nature, anysuitable combination of the foregoing, or equivalents thereof. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical device havinga storage capability, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiberbased device, a portable compact disc read-only memory (CDROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium is any tangible medium that can containor store a program for use by, or in connection with, an instructionexecution system, apparatus, or device.

In some illustrative embodiments, the computer readable medium is anon-transitory computer readable medium. A non-transitory computerreadable medium is any medium that is not a disembodied signal orpropagation wave, i.e. pure signal or propagation wave per se. Anon-transitory computer readable medium may utilize signals andpropagation waves, but is not the signal or propagation wave itself.Thus, for example, various forms of memory devices, and other types ofsystems, devices, or apparatus, that utilize signals in any way, suchas, for example, to maintain their state, may be considered to benon-transitory computer readable media within the scope of the presentdescription.

A computer readable signal medium, on the other hand, may include apropagated data signal with computer readable program code embodiedtherein, for example, in a baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, including, but notlimited to, electro-magnetic, optical, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Similarly, a computer readable storage medium is any computer readablemedium that is not a computer readable signal medium.

Computer code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination thereof.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java™, Smalltalk™, C++, or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to the illustrativeembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions thatimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

FIGS. 1-3 are directed to describing an example Question/Answer,Question and Answer, or Question Answering (QA) system, methodology, andcomputer program product with which the mechanisms of the illustrativeembodiments may be implemented. As will be discussed in greater detailhereafter, the illustrative embodiments may be integrated in, and mayaugment and extend the functionality of, these QA mechanisms with regardto routing of input questions to one or more QA system pipelines of a QAsystem based on predictive load balancing which in turn is based on adetermined question difficulty. The question difficulty is representedby a feature pattern or rule that indicates for a specific combinationof features, a corresponding predicted amount of time needed to generatean answer of a minimum threshold level of confidence.

Since the mechanisms of the illustrative embodiments improve and augmenta QA system, it is important to first have an understanding of howquestion and answer generation in a QA system may be implemented beforedescribing how the mechanisms of the illustrative embodiments areintegrated in and augment such QA systems. It should be appreciated thatthe QA mechanisms described in FIGS. 1-3 are only examples and are notintended to state or imply any limitation with regard to the type of QAmechanisms with which the illustrative embodiments may be implemented.Many modifications to the example QA system shown in FIGS. 1-3 may beimplemented in various embodiments of the present invention withoutdeparting from the spirit and scope of the present invention.

QA mechanisms operate by accessing information from a corpus of data orinformation (also referred to as a corpus of content), analyzing it, andthen generating answer results based on the analysis of this data.Accessing information from a corpus of data typically includes: adatabase query that answers questions about what is in a collection ofstructured records, and a search that delivers a collection of documentlinks in response to a query against a collection of unstructured data(text, markup language, etc.). Conventional question answering systemsare capable of generating answers based on the corpus of data and theinput question, verifying answers to a collection of questions for thecorpus of data, correcting errors in digital text using a corpus ofdata, and selecting answers to questions from a pool of potentialanswers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, maydetermine use cases for products, solutions, and services described insuch content before writing their content. Consequently, the contentcreators may know what questions the content is intended to answer in aparticular topic addressed by the content. Categorizing the questions,such as in terms of roles, type of information, tasks, or the like,associated with the question, in each document of a corpus of data mayallow the QA system to more quickly and efficiently identify documentscontaining content related to a specific query. The content may alsoanswer other questions that the content creator did not contemplate thatmay be useful to content users. The questions and answers may beverified by the content creator to be contained in the content for agiven document. These capabilities contribute to improved accuracy,system performance, machine learning, and confidence of the QA system.Content creators, automated tools, or the like, may annotate orotherwise generate metadata for providing information useable by the QAsystem to identify these question and answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The illustrative embodimentsleverage the work already done by the QA system to reduce thecomputation time for subsequent processing of questions that are similarto questions already processed by the QA system.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion and answer (QA) system 100 in a computer network 102. Oneexample of a question and answer generation which may be used inconjunction with the principles described herein is described in U.S.Patent Application Publication No. 2011/0125734, which is hereinincorporated by reference in its entirety. The QA system 100 may beimplemented on one or morecomputing devices 104 (comprising one or moreprocessors and one or more memories, and potentially any other computingdevice elements generally known in the art including buses, storagedevices, communication interfaces, and the like) connected to thecomputer network 102. The network 102 may include multiple computingdevices 104 in communication with each other and with other devices orcomponents via one or more wired and/or wireless data communicationlinks, where each communication link may comprise one or more of wires,routers, switches, transmitters, receivers, or the like. The QA system100 and network 102 may enable question/answer (QA) generationfunctionality for one or more QA system users via their respectivecomputing devices 110-112. Other embodiments of the QA system 100 may beused with components, systems, sub-systems, and/or devices other thanthose that are depicted herein.

The QA system 100 may be configured to implement a QA system pipeline108 that receive inputs from various sources. For example, the QA system100 may receive input from the network 102, a corpus of electronicdocuments 106, QA system users, or other data and other possible sourcesof input. In one embodiment, some or all of the inputs to the QA system100 may be routed through the network 102. The various computing devices104 on the network 102 may include access points for content creatorsand QA system users. Some of the computing devices 104 may includedevices for a database storing the corpus of data 106 (which is shown asa separate entity in FIG. 1 for illustrative purposes only). Portions ofthe corpus of data 106 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 1. The network 102 may includelocal network connections and remote connections in various embodiments,such that the QA system 100 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document may include any file, text, article, or sourceof data for use in the QA system 100. QA system users may access the QAsystem 100 via a network connection or an Internet connection to thenetwork 102, and may input questions to the QA system 100 that may beanswered by the content in the corpus of data 106. In one embodiment,the questions may be formed using natural language. The QA system 100may interpret the question and provide a response to the QA system user,e.g., QA system user 110, containing one or more answers to thequestion. In some embodiments, the QA system 100 may provide a responseto users in a ranked list of candidate answers.

The QA system 100 implements a QA system pipeline 108 which comprises aplurality of stages for processing an input question, the corpus of data106, and generating answers for the input question based on theprocessing of the corpus of data 106. The QA system pipeline 108 will bedescribed in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The Watson™ QA system may receive aninput question which it then parses to extract the major features of thequestion, that in turn are then used to formulate queries that areapplied to the corpus of data. Based on the application of the queriesto the corpus of data, a set of hypotheses, or candidate answers to theinput question, are generated by looking across the corpus of data forportions of the corpus of data that have some potential for containing avaluable response to the input question.

The Watson™ QA system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the Watson™ QA system has regarding the evidence that the potentialresponse, i.e. candidate answer, is inferred by the question. Thisprocess may be repeated for each of the candidate answers until theWatson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as server 104 orclient 110 in FIG. 1, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention may be located. In one illustrative embodiment, FIG. 2represents a server computing device, such as a server 104, which, whichimplements a QA system 100 and QA system pipeline 108 augmented toinclude the additional mechanisms of the illustrative embodimentsdescribed hereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 may be implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage may be implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.may be executed on one or more processors of one or more data processingsystems or devices and may utilize or operate on data stored in one ormore data storage devices, memories, or the like, on one or more of thedata processing systems. The QA system pipeline of FIG. 3 may beaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user may input, via a userinterface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA system pipeline500, i.e. the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

The identified major features may then be used during the questiondecomposition stage 330 to decompose the question into one or morequeries that may be applied to the corpora of data/information 345, or acorpus 347 within the corpora 345, in order to generate one or morehypotheses. The queries may be generated in any known or later developedquery language, such as the Structure Query Language (SQL), or the like.The queries may be applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpora of data/information 345. That is, these varioussources themselves, collections of sources, and the like, may eachrepresent a different corpus 347 within the corpora 345. There may be adifferent corpus 347 defined for different collections of documentsbased on various criteria depending upon the particular implementation.For example, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 347 within the corpora 345.

The queries may be applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries being applied to the corpus ofdata/information at the hypothesis generation stage 340 to generateresults identifying potential hypotheses for answering the inputquestion which can be evaluated. That is, the application of the queriesresults in the extraction of portions of the corpus of data/informationmatching the criteria of the particular query. These portions of thecorpus may then be analyzed and used, during the hypothesis generationstage 540, to generate hypotheses for answering the input question.These hypotheses are also referred to herein as “candidate answers” forthe input question. For any input question, at this stage 340, there maybe hundreds of hypotheses or candidate answers generated that may needto be evaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this mayinvolve using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not, ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis.

In the synthesis stage 360, the large number of relevance scoresgenerated by the various reasoning algorithms may be synthesized intoconfidence scores for the various hypotheses. This process may involveapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QAsystem and/or dynamically updated, as described hereafter. The weightedscores may be processed in accordance with a statistical model generatedthrough training of the QA system that identifies a manner by whichthese scores may be combined to generate a confidence score or measurefor the individual hypotheses or candidate answers. This confidencescore or measure summarizes the level of confidence that the QA systemhas about the evidence that the candidate answer is inferred by theinput question, i.e. that the candidate answer is the correct answer forthe input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which may compare theconfidence scores and measures, compare them against predeterminedthresholds, or perform any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe answer to the input question. The hypotheses/candidate answers maybe ranked according to these comparisons to generate a ranked listing ofhypotheses/candidate answers (hereafter simply referred to as “candidateanswers”). From the ranked listing of candidate answers, at stage 380, afinal answer and confidence score, or final set of candidate answers andconfidence scores, may be generated and output to the submitter of theoriginal input question.

In accordance with the illustrative embodiments, the QA system 100 inFIG. 1 may implement a plurality of QA system pipelines 108, such as theQA system pipeline shown in FIG. 3, for example. Each QA system pipeline108 may be executed on a separate processor, computing device, computingsystem, or the like, referred to as “nodes” herein. The QA system 100may route input questions to the various QA system pipelines 108 tobalance the load of the processing of the input questions across thevarious QA system pipelines 108. This load balancing, in accordance withthe mechanisms of the illustrative embodiments, is performed inaccordance with a determined question difficulty of the input questionand the current loads on the QA system pipelines 108. The determinedquestion difficulty is based on feature patterns identified duringtraining and a corresponding amount of time and resources used to answerquestions having such feature patterns, as determined during training ofthe QA system 100. Thus, in order to facilitate such routing of inputquestions using load balancing that is based on predicted questiondifficulty, the mechanisms of the illustrative embodiments utilize atraining phase and a runtime production system phase. Each of thesephases and the primary operational components utilized during thesephases will be described in greater detail hereafter with reference toFIGS. 4 and 5.

FIG. 4 is an example diagram of the primary operational components, andtheir operation, during a training phase of operation of a QA system inaccordance with one illustrative embodiment. As shown in FIG. 4, theprimary operational elements of a QA system 400 used during a trainingphase of operation comprise a question analysis engine 410, a QA systempipeline 420, a question metadata storage device 430, a data analysisengine 440, and a question difficultly prediction rule storage device450. The question analysis engine 410 is responsible for extractingfeatures from an input question and store these features in a datastructure associated with the input question in the question metadatastorage device 430 for further analysis. The QA system pipeline 420performs the functions as previously described above with regard to FIG.3 to process an input question and generate an answer for the inputquestion. In accordance with the mechanisms of the illustrativeembodiments the QA system pipeline may be augmented to record aprocessing time required to process the input question. This may beaccomplished by starting a timer when processing of the input questionis initiated, or storing timestamps at the beginning and end ofprocessing such that the difference between the timestamps may be usedto determine a time for processing the input question.

The question metadata storage device 430 stores metadata data structuresfor questions input to the QA system. The metadata stored in thequestion metadata storage device 430 may be extracted from the questionby the question analysis engine 410 and stored in the data structure460. In addition, processing time data required to process the inputquestion may be stored in the data structure by the QA system pipeline420 once the question is processed by the QA system pipeline 420.

The data analysis engine 440 analyzes the metadata data structures 460in the question metadata storage device 430 to identify feature patternsand related load characteristics. The feature patterns and related loadcharacteristics may be used as a basis for defining question difficultyprediction rules that may be applied during runtime operation to predictthe difficulty of answering an input question having a similar featurepattern. These question difficulty prediction rules are stored in thequestion difficultly prediction rule storage device 450 for later useduring runtime operation.

As shown in FIG. 4, during a training phase of operation of the QAsystem 400, a set of training input questions 405 is used as a basis fortraining the QA system 400 with regard to determining question featurepatterns and corresponding processing time data. The questions may bespecific to a particular domain, e.g., subject matter or questionclassification, so as to train the QA system with regard to a specificdomain. It should be appreciated that multiple training input questionsets may be utilized so as to train the QA system for multiple domains.A separate set of question difficulty prediction rules may be generatedfor each domain in accordance with the mechanisms of the illustrativeembodiments.

For a particular set of training input questions 405, the inputquestions are submitted to the QA system 400 and are initially processedby the question analysis engine 410. For a given input question in theset 405, the question analysis engine 410 analyzes the question toextract various features of the question for use in generating metadatadescribing the question being processed. In addition, the questionanalysis engine 410 monitors the time required to extract various onesof these features and stores this timing data along with the extractedfeatures as part of a metadata data structure associated with thequestion in the question metadata storage device 430.

The types of features extracted, and timing data collected, by thequestion analysis engine 410 may vary depending upon the particularimplementation. In one illustrative embodiment, the extracted featuresinclude domain specific question artifacts, the number of sentences inthe question, the number of words in each sentence, the total number ofwords in the question, the length of the words or average length of thewords, the overall length of the question, a focus of the question, alexical answer type (LAT) of the question, a Question Classification(QClass) of the question, and a Question Section (QSection) of thequestion. Features such as the focus, LAT, QClass, and QSection areextracted by the QA system 400 during runtime operation to analyze aninput question and generate queries to be applied against a corpus ofdocuments to obtain candidate answers, and ultimately a final answer, tothe input question. The focus of a question is the portion of thequestion that references the answer, e.g., the word “he” in the question“was he the greatest football player?” is the focus of the questionindicating that a male person is the focus of the question. The LATrefers to the terms in the question that indicates what type of entityis being asked for, e.g., in the statement “he liked to write poems” theLAT is “poets”. The QClass is the type the question belongs to, e.g.,name, definition, category, abbreviation, number, date, etc., e.g., ifthe question is “who was the first President of the United States?,” theQClass is a name since the question is looking for a name as the answer.The QSection refers to question fragments that require specialprocessing and inform lexical restraints on the answer, e.g., “this 7letter word . . . ” provides a lexical restraint on the answer being a 7letter word.

Because features such as the focus, LAT, QClass, and QSection areextracted by the QA system 400 during runtime operation when processingquestions, the timing related to the extraction of these featuresaffects the timing required to process the question and is thus, partlyindicative of the question difficulty. Hence, as part of the analysisperformed by the question analysis engine 410, the question analysisengine monitors the amount of time required to extract these featuresfrom the input question and records this timing data in the metadatadata structure for the question stored in the question metadata storage430. Thus, for example, the time to compute the focus, the time tocompute the LAT, the time to compute the QClass, and the time to computethe QSection may be monitored and recorded, along with the extractedfeatures, in the data structure 460 in the question metadata storagedevice 430.

After the initial processing of the training question by the questionanalysis engine 410, the training question is submitted to a QA systempipeline 420 for processing. The QA system pipeline 420 processes thetraining question in a normal manner, such as described above withregard to FIG. 3, with the exception that in addition to this normalmanner of processing, the QA system pipeline 420 monitors the amount oftime required to complete the processing of the training question. Thisamount of time is recorded in the metadata data structure 460 along withthe other question metadata. It should be appreciated that there may bea separate question metadata data structure 460 generated for eachtraining question submitted to the QA system 400.

These metadata data structures 460 may later be analyzed by the dataanalysis engine 440 to identify patterns in the features of the trainingquestions that are indicative of predictive feature patterns inquestions. That is, the feature patterns are indicative of questionsthat may require a substantially similar amount of time to process. Bydetermining these feature patterns and associating them withcorresponding predicted amounts of time for processing, a difficulty ofquestions matching the feature pattern may be identified andcorresponding question difficulty prediction rules may be generated andstored in the question difficultly prediction rule storage device 450.That is, questions requiring a larger amount of time to process are moredifficult to process than questions requiring relatively smaller amountsof time to process. Thus, by matching the features of an input questionto feature patterns stored in question difficulty prediction rules,corresponding predicted amounts of time for processing the question maybe determined and thus, an expected level of difficulty of the questionmay be predicted. In some illustrative embodiments, various thresholdsmay be established for processing times so as to classify questions intoclasses of difficulty, e.g., easy, medium, or hard. Theseclassifications may be stored in conjunction with the questiondifficulty prediction rules and may be used during runtime to determinerouting of questions, as described hereafter.

With regard to the actual identification of feature patterns within thefeatures specified in question metadata data structures, various logicmay be applied to one or more of the features in the metadata datastructures to identify feature patterns. For example, logic may beemployed for determining which metadata structures have an averagelength of words greater than 4 characters. Of those metadata datastructures, a determination may be made as to which have a focus of “A”.Of those, a determination may be made as to which have a LAT of “B”. Ofthose, the average time to answer the question may be calculated. Theresult is a pattern indicant that for questions having a focus of A anda LAT of B, the average time to answer the question is predicted to be“X”. This is but a simple example of one analysis of the metadata thatmay be employed. Of course the various types of the patterns that arerecognizable and the types of logic employed to recognize such patternsmay take many different forms depending upon the particularimplementation. For example, various logic may be used to analyze aplurality of question metadata data structures to identify trends inthis metadata and identify similar characteristics. For example, oneembodiment may determine that a plurality of questions that took lessthan a particular amount of time to process were all directed to aparticular domain and/or had a LAT that took more than N milliseconds tocompute and/or 80% of them were less than N number of words in length,etc. Any trend analysis and characteristic similarity identificationmechanism may be used without departing from the spirit and scope of thepresent invention.

Thus, the illustrative embodiments provide mechanisms for generatingquestion difficulty prediction rules based on identified patterns infeatures extracted from training questions and corresponding times forprocessing these training questions. These question difficultyprediction rules are then applied, during runtime operation, to newlysubmitted questions to predict their difficulty and perform loadbalancing routing of questions to QA system pipelines based on thepredicted difficulty of the question.

FIG. 5 is an example diagram illustrating the primary operationalelements of a QA system during runtime operation using questiondifficulty prediction rules generated as part of a training operation inaccordance with one illustrative embodiment. As shown in FIG. 5, the QAsystem 500 comprises a question analysis engine 510, a load balancingrouter 520, a plurality of QA system pipelines 530-532, a data analysisengine 540, a question metadata storage device 550, and a questiondifficulty rule storage device 560. The elements 510, 530-532, 540, 550,and 560 operate in a similar manner to corresponding elements in FIG. 4.The primary difference in operation here, however is that there aremultiple QA system pipelines available to process the input question andthe load balancing router 520 performs load balancing routing of inputquestions to the QA system pipelines 530-532 based on current loadlevels of the QA system pipelines 530-532 as reported by the QA systempipelines 530-532 or otherwise determined by the load balancing router520, and the question difficulty as determined by applying the questiondifficulty prediction rules to the extracted features of the inputquestion as extracted by the question analysis engine 510.

As a further functionality, dynamic updating of the difficultyprediction rules may be made possible by again storing question metadatagenerated by the question analysis engine 510 during runtime operationin the question metadata storage device 550 as well as the times ofprocessing of questions reported by the various QA system pipelines530-532. The data analysis engine 540 may, periodically, or in responseto the occurrence of a particular event, e.g., a predetermined number ofquestions having been process, process the metadata data structures inthe question metadata storage device 550 to determine if updates toquestion difficulty rules are appropriate based on runtime processing ofquestions. Such updates may be based on the newly acquired metadata datastructures generated during runtime as well as the question difficultyprediction rules generated during training, e.g., the predictedprocessing time may be generated as an average of the predictedprocessing time in the question difficulty prediction rule and theactual times of processing stored in the metadata data structures forquestions having feature patterns matching the question difficultyprediction rule.

In operation, when an input question is received by the QA system 500,the question is first analyzed by the question analysis engine 510 toextract features and store these features in a metadata data structure570 in the question metadata storage device 550. In addition theextracted features may be provided to the load balancing router 520 toperform a lookup of a question difficulty prediction rule that has amatching pattern of features to those of the input question. Thematching question difficulty prediction rule, if there is one, is usedto determine the level of difficulty of processing the input questionand/or the predicted amount of time required to process the inputquestion. Using this information along with current load information forthe QA system pipelines 530-532, stored in current load storage device580, the load balancing router may select a QA system pipeline 530-532to handle processing the input question so as to balance the load acrossthe nodes associated with the QA system pipelines 530-532. The loadbalancing router 520 may then route the input question to the selectedQA system pipeline 530-532. Once the selected QA system pipeline 530-532finishes processing the input question, the time required to process theinput question may be stored in the metadata data structure 570associated with the question for later analysis by the data analysisengine 540 when updating the question difficulty prediction rules.

Thus, the illustrative embodiments provide mechanisms for generatingpredictions of question difficulty based on patterns of features ofinput questions and using these predictions of question difficulty todetermine load balancing routing to be applied to the input questions.Based on the determined load balancing routing, the input question maybe routed to one of a plurality of QA system pipelines so as to balancethe load across the nodes hosting the QA system pipelines. In this way,the load balancing helps achieve service level agreement requirementsand other performance goals by providing load balancing to ensure thatsuch requirements are able to be achieved.

FIG. 6 is a flowchart outlining an example operation for performingquestion difficulty prediction training of a QA system in accordancewith one illustrative embodiment. As shown, in FIG. 6, the operationstarts by inputting a set of training questions to a QA system (step610). For the next question in the set of training questions (step 620),the question is analyzed to extract features from the question (step630) and store the extracted features along with timing informationindicating the amount of time to extract such features by the questionanalysis engine in a question metadata data structure (step 640). Thequestion is provided to a QA system pipeline for processing (step 650)and the amount of time required to process the question via the QAsystem pipeline is recorded in the question metadata data structure(step 660).

A determination is made as to whether this was the last question in theset of training questions (step 670). If not, the operation returns tostep 620 and is repeated for the next question in the set of trainingquestions. If this was the last question in the set of trainingquestions, then a data analysis engine analyzes the question metadatadata structures for the questions in the set of training questions toidentify feature patterns (step 680). For each identified featurepattern, steps 690-694 are performed. That is, the timing data in thequestion metadata data structures matching the feature pattern isanalyzed to determine a predicted amount time for processing questionsmatching the feature pattern (step 690). The predicted amount of timefor processing questions matching the feature pattern may then becompared against one or more thresholds of difficulty classification togenerate a difficulty classification for questions matching the featurepattern (step 692). The feature pattern, predicted time for processingof questions matching the feature pattern, and the question difficultyclassification may then be stored as part of a question difficultyprediction rule in a question difficulty prediction rule data structurefor later use during runtime operations (step 694). Once each of theidentified feature patterns is processed in this manner, the operationthen terminates.

FIG. 7 is a flowchart outlining an example operation for performingruntime load balancing routing based on predicted question difficulty inaccordance with one illustrative embodiment. As shown in FIG. 7, theoperation starts by receiving, in the QA system, an input question forprocessing (step 710). The input question is analyzed to extractfeatures from the input question (step 720). Based on the extractedfeatures, a search of a question difficulty prediction rule having apattern of features matched by the extracted features of the inputquestion is performed (step 730). Based on the matching questiondifficulty prediction rule, if any, a question difficulty and timerequired for processing the question is identified (step 740). It shouldbe appreciated that if a matching question difficulty prediction rulecannot be found, a default predicted difficulty may be utilized, e.g.,medium difficulty.

A current load of each of a plurality of QA system pipelines isretrieved (step 750) and is used along with the predicted difficulty ofthe input question and/or required time of processing to select a QAsystem pipeline to process the input question that balances the loadacross the nodes hosting the plurality of QA system pipelines (step760). The input question is then routed to the selected QA systempipeline (step 770) and the input question is thereafter processed bythe selected QA system pipeline (step 780). It should be appreciatedthat, as previously described above, in addition to routing the inputquestion to a selected QA system pipeline, during runtime operation adynamic updating of the question difficulty prediction rules may befacilitated by storing the extracted feature metadata from the inputquestion and the time actually used to process the input question by theselected QA system pipeline. This metadata may be periodically processedby the data analysis engine to perform such dynamic updating.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method, in a data processing system, for processing an inputquestion in a Question and Answer (QA) system, implemented by the dataprocessing system, having a plurality of QA system pipelines, the methodcomprising: receiving, by the data processing system, an input questionfor processing by the QA system; determining, by the data processingsystem, a predicted question difficulty for generating an answer to theinput question based on at least one feature extracted from the inputquestion and a correlation of the at least one feature with a predictedlevel of question difficulty; selecting, by the data processing system,a QA system pipeline from the plurality of QA system pipelines based onthe predicted question difficulty; routing, by the data processingsystem, the input question to the selected QA system pipeline forprocessing; and processing, by the data processing system, the inputquestion by the selected QA system pipeline to generate an answer forthe input question.
 2. The method of claim 1, wherein the predictedquestion difficulty is indicative of a predicted amount of time requiredto process the input question via a QA system pipeline in the pluralityof QA system pipelines.
 3. The method of claim 1, wherein selecting theQA system pipeline from the plurality of QA system pipelines based onthe predicted question difficulty further comprises selecting the QAsystem pipeline based on a current load of each of the QA systempipelines in the plurality of QA system pipelines.
 4. The method ofclaim 3, wherein the current load of a QA system pipeline is determinedbased on one or more determined levels of difficulty for one or morequestions currently being processed by the QA system pipeline.
 5. Themethod of claim 1, further comprising: training the QA system usingtraining questions, wherein the training of the QA system comprisesextracting features from the training questions, identifying similarfeatures in the training questions to thereby generate one or moregroups of training questions having similar features, identifying alevel of difficulty of processing each of the one or more groups oftraining questions, and generating a rule correlating the extractedfeatures to the identified level of difficulty; and storing thegenerated rule as a prediction rule in a prediction rule storage device,wherein determining the predicted question difficulty of the inputquestion comprises applying stored prediction rules in the predictionrule storage device to the input features extracted from the inputquestion to predict the level of difficulty of the input question. 6.The method of claim 1, wherein the level of difficulty of processingeach of the one or more groups of training questions comprisesdetermining the level of difficulty based on a combination of an amountof time required to extract features from a question and an amount oftime required to generate an answer to the question from a corpus ofdata.
 7. The method of claim 4, wherein the training of the QA systemusing training questions comprises performing domain specific trainingof the QA system for a plurality of domains of question subject matter.8. The method of claim 1, wherein determining a predicted questiondifficulty of the input question comprises: extracting, from the inputquestion, one or more features of the input question to generate one ormore extracted features; comparing the one or more extracted features toone or more patterns of features; identifying a matching pattern offeatures based on results of the comparison of the one or more extractedfeatures to the one or more patterns of features; and identifying anindicator of question difficulty corresponding to the matching patternof features.
 9. The method of claim 8, wherein the one or more extractedfeatures comprises at least one of a number of sentences in the inputquestion, a statistical measure of a number of words in sentences of theinput question, a total number of words in the input question, astatistical measure of a length of words in the input question, a lengthof the input question, a focus, a lexical answer type, a questionclassification, or a question section.
 10. The method of claim 8,wherein the indicator of question difficulty comprises at least one of atime to process questions having extracted features matching thematching pattern or a category of difficulty determined based on a timeto process questions having extracted features matching the matchingpattern, and wherein the time to process questions having extractedfeatures matching the matching pattern is measured as an amount of timerequired to generate an answer from a corpus of data combined with oneor more of a time to compute a focus of a question corresponding to thematching pattern, a time to compute a lexical answer type of a questioncorresponding to the matching pattern, a time to compute a questionclassification of a question corresponding to the matching pattern, or atime to compute a question section of a question corresponding to thematching pattern.
 11. A computer program product comprising a computerreadable storage medium having a computer readable program storedtherein, wherein the computer readable program, when executed on a dataprocessing system implementing a Question and Answer (QA) system havinga plurality of QA system pipelines, causes the data processing systemto: receive an input question for processing by the QA system; determinea predicted question difficulty for generating an answer to the inputquestion based on at least one feature extracted from the input questionand a correlation of the at least one feature with a predicted level ofquestion difficulty; select a QA system pipeline from the plurality ofQA system pipelines based on the predicted question difficulty; routethe input question to the selected QA system pipeline for processing;and process the input question by the selected QA system pipeline togenerate an answer for the input question.
 12. The computer programproduct of claim 11, wherein the predicted question difficulty isindicative of a predicted amount of time required to process the inputquestion via a QA system pipeline in the plurality of QA systempipelines.
 13. The computer program product of claim 11, wherein thecomputer readable program further causes the data processing system toselect the QA system pipeline from the plurality of QA system pipelinesbased on the predicted question difficulty further at least by selectingthe QA system pipeline based on a current load of each of the QA systempipelines in the plurality of QA system pipelines.
 14. The computerprogram product of claim 13, wherein the current load of a QA systempipeline is determined based on one or more determined levels ofdifficulty for one or more questions currently being processed by the QAsystem pipeline.
 15. The computer program product of claim 11, whereinthe computer readable program further causes the data processing systemto: train the QA system using training questions, wherein the trainingof the QA system comprises extracting features from the trainingquestions, identifying similar features in the training questions tothereby generate one or more groups of training questions having similarfeatures, identifying a level of difficulty of processing each of theone or more groups of training questions, and generating a rulecorrelating the extracted features to the identified level ofdifficulty; and store the generated rule as a prediction rule in aprediction rule storage device, wherein determining the predictedquestion difficulty of the input question comprises applying storedprediction rules in the prediction rule storage device to the inputfeatures extracted from the input question to predict the level ofdifficulty of the input question.
 16. The computer program product ofclaim 11, wherein the level of difficulty of processing each of the oneor more groups of training questions comprises determining the level ofdifficulty based on a combination of an amount of time required toextract features from a question and an amount of time required togenerate an answer to the question from a corpus of data.
 17. Thecomputer program product of claim 14, wherein the computer readableprogram further causes the data processing system to train the QA systemusing training questions at least by performing domain specific trainingof the QA system for a plurality of domains of question subject matter.18. The computer program product of claim 11, wherein the computerreadable program further causes the data processing system to determinea predicted question difficulty of the input question at least by:extracting, from the input question, one or more features of the inputquestion to generate one or more extracted features; comparing the oneor more extracted features to one or more patterns of features;identifying a matching pattern of features based on results of thecomparison of the one or more extracted features to the one or morepatterns of features; and identifying an indicator of questiondifficulty corresponding to the matching pattern of features.
 19. Thecomputer program product of claim 18, wherein the indicator of questiondifficulty comprises at least one of a time to process questions havingextracted features matching the matching pattern or a category ofdifficulty determined based on a time to process questions havingextracted features matching the matching pattern, and wherein the timeto process questions having extracted features matching the matchingpattern is measured as an amount of time required to generate an answerfrom a corpus of data combined with one or more of a time to compute afocus of a question corresponding to the matching pattern, a time tocompute a lexical answer type of a question corresponding to thematching pattern, a time to compute a question classification of aquestion corresponding to the matching pattern, or a time to compute aquestion section of a question corresponding to the matching pattern.20. A data processing system comprising: a processor; and a memorycoupled to the processor, wherein the memory comprises instructionswhich, when executed by the processor, cause the processor to: receivean input question for processing by a Question and Answer (QA) system;determine a predicted question difficulty for generating an answer tothe input question based on at least one feature extracted from theinput question and a correlation of the at least one feature with apredicted level of question difficulty; select a QA system pipeline froma plurality of QA system pipelines based on the predicted questiondifficulty; route the input question to the selected QA system pipelinefor processing; and process the input question by the selected QA systempipeline to generate an answer for the input question.